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A Unified Factors Analysis Framework for Discriminative Feature Extraction and Object Recognition

机译：歧视性特征提取与目标识别的统一因素分析框架

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摘要

Various methods for feature extraction and dimensionality reduction have been proposed in recent decades, including supervised and unsupervised methods and linear and nonlinear methods. Despite the different motivations of these methods, we present in this paper a general formulation known as factor analysis to unify them within a common framework. During factor analysis, an object can be seen as being comprised of content and style factors, and the objective of feature extraction and dimensionality reduction is to obtain the content factor without style factor. There are two vital steps in factor analysis framework; one is the design of factor separating objective function, including the design of partition and weight matrix, and the other is the design of space mapping function. In this paper, classical Linear Discriminant Analysis (LDA) and Locality Preserving Projection (LPP) algorithms are improved based on factor analysis framework, and LDA based on factor analysis (FA-LDA) and LPP based on factor analysis (FA-LPP) are proposed. Experimental results show the superiority of our proposed approach in classification performance compared to classical LDA and LPP algorithms.
机译：近几十年来，提出了多种特征提取和降维方法，包括有监督和无监督方法以及线性和非线性方法。尽管这些方法有不同的动机，但我们在本文中提出了一种通用的公式，称为因子分析，以将它们统一在一个通用框架内。在因子分析过程中，可以将对象视为由内容和样式因子组成，特征提取和降维的目的是获得没有样式因子的内容因子。因子分析框架有两个重要步骤；一种是因子分离目标函数的设计，包括分区和权重矩阵的设计，另一种是空间映射函数的设计。本文基于因子分析框架对经典线性判别分析（LDA）和局部保留投影（LPP）算法进行了改进，基于因子分析的LDA（FA-LDA）和基于因子分析的LPP（FA-LPP）得到了改进。建议。实验结果表明，与经典的LDA和LPP算法相比，我们提出的方法在分类性能上具有优势。

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  • 来源
    《Mathematical Problems in Engineering》 |2016年第4期|9347838.1-9347838.12|共12页
  • 作者

    Hao Ningbo; Yang Jie; Liao Haibin; Dai Wenhua;

  • 作者单位

    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China|Huanghuai Univ, Sch Int Coll, Zhumadian 463000, Henan, Peoples R China;

    Wuhan Univ Technol, Sch Informat Engn, Wuhan 430070, Peoples R China;

    Hubei Univ Sci & Technol, Sch Comp Sci & Technol, Xianning 437100, Peoples R China|Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China;

    Hubei Univ Sci & Technol, Sch Comp Sci & Technol, Xianning 437100, Peoples R China;

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